Results food data clustering DEL

All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))

DEC_Embedding = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding.csv')
head(DEC_Embedding)

resultft_DEL_all <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all$farmlive[resultft_DEL_all$farmlive == ""] <- NA
resultft_DEL_all <-  resultft_DEL_all %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)

dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 

dist_plot_clust(original_data = resultft_DEL_all, selected_variable = age)

Density plot shoiwing the age distribution for each cluster

resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
age_g <- ggplot(resultft_DEL_all, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
ggplotly(age_p)

g <- ggplot(resultft_DEL_all, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 
ggplotly(s)

table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters

	0 (N=526)	1 (N=328)	2 (N=910)	3 (N=108)	Total (N=1872)	p value
sIgE_f1						< 0.001
Mean (SD)	0.030 (0.122)	0.068 (0.493)	0.000 (0.000)	1.076 (7.174)	0.082 (1.747)
Range	0.000 - 1.060	0.000 - 8.179	0.000 - 0.000	0.000 - 73.692	0.000 - 73.692
sIgE_f2						< 0.001
Mean (SD)	0.038 (0.138)	0.082 (0.364)	0.000 (0.000)	0.610 (2.148)	0.060 (0.558)
Range	0.000 - 1.091	0.000 - 3.807	0.000 - 0.000	0.000 - 13.623	0.000 - 13.623
sIgE_f3						< 0.001
Mean (SD)	0.004 (0.013)	0.008 (0.022)	0.000 (0.000)	0.113 (0.261)	0.009 (0.069)
Range	0.000 - 0.080	0.000 - 0.137	0.000 - 0.000	0.000 - 1.332	0.000 - 1.332
sIgE_f4						< 0.001
Mean (SD)	0.018 (0.095)	0.093 (0.379)	0.000 (0.000)	0.886 (1.944)	0.073 (0.534)
Range	0.000 - 1.280	0.000 - 4.347	0.000 - 0.000	0.000 - 12.512	0.000 - 12.512
sIgE_f13						< 0.001
Mean (SD)	0.030 (0.091)	0.220 (0.759)	0.000 (0.001)	4.809 (19.740)	0.324 (4.861)
Range	0.000 - 0.927	0.000 - 11.866	0.000 - 0.020	0.000 - 149.746	0.000 - 149.746
sIgE_f14						< 0.001
Mean (SD)	0.004 (0.019)	0.033 (0.125)	0.000 (0.000)	0.684 (1.847)	0.046 (0.472)
Range	0.000 - 0.310	0.000 - 1.086	0.000 - 0.000	0.000 - 12.386	0.000 - 12.386
sIgE_f17						< 0.001
Mean (SD)	0.312 (0.873)	2.484 (3.286)	0.000 (0.013)	22.025 (21.996)	1.794 (7.461)
Range	0.000 - 11.197	0.000 - 13.628	0.000 - 0.398	0.000 - 111.259	0.000 - 111.259
sIgE_f18						< 0.001
Mean (SD)	0.017 (0.168)	0.017 (0.039)	0.001 (0.025)	0.579 (4.510)	0.042 (1.091)
Range	0.000 - 2.901	0.000 - 0.384	0.000 - 0.686	0.000 - 46.879	0.000 - 46.879
sIgE_f20						< 0.001
Mean (SD)	0.011 (0.027)	0.094 (0.142)	0.000 (0.001)	1.106 (1.703)	0.083 (0.485)
Range	0.000 - 0.193	0.000 - 0.915	0.000 - 0.015	0.000 - 9.959	0.000 - 9.959
sIgE_f36						< 0.001
Mean (SD)	0.011 (0.025)	0.067 (0.129)	0.000 (0.002)	0.412 (1.090)	0.039 (0.283)
Range	0.000 - 0.225	0.000 - 1.051	0.000 - 0.045	0.000 - 7.754	0.000 - 7.754
gender2						< 0.001
females	329 (62.5%)	185 (56.4%)	453 (49.8%)	55 (50.9%)	1022 (54.6%)
males	197 (37.5%)	143 (43.6%)	457 (50.2%)	53 (49.1%)	850 (45.4%)
age						< 0.001
Mean (SD)	47.196 (15.214)	48.255 (15.130)	51.199 (15.617)	41.367 (14.267)	48.991 (15.551)
Range	18.146 - 76.877	19.058 - 78.075	18.875 - 77.746	19.415 - 77.130	18.146 - 78.075
bmi2						< 0.001
Mean (SD)	27.698 (3.266)	30.328 (6.497)	24.411 (2.776)	26.762 (4.942)	26.507 (4.540)
Range	18.904 - 34.816	18.290 - 50.058	16.975 - 31.556	17.915 - 38.955	16.975 - 50.058
farmlive
	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
no	460 (87.5%)	292 (89.0%)	789 (86.7%)	107 (99.1%)	1648 (88.0%)
yes	66 (12.5%)	36 (11.0%)	121 (13.3%)	1 (0.9%)	224 (12.0%)
family_allergy_hist						< 0.001
no	229 (43.5%)	132 (40.2%)	633 (69.6%)	28 (25.9%)	1022 (54.6%)
yes	297 (56.5%)	196 (59.8%)	277 (30.4%)	80 (74.1%)	850 (45.4%)

# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

resultft_DEL_all$cluster <- as.factor(resultft_DEL_all$cluster)
catdes(resultft_DEL_all, 16)

Random Data clusters with DEL (Deep Embedding Clustering layer)

#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <-  result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters

	0 (N=285)	1 (N=472)	2 (N=110)	3 (N=236)	Total (N=1103)	p value
sIgE_f1						< 0.001
Mean (SD)	0.001 (0.009)	0.000 (0.000)	0.067 (0.291)	0.044 (0.149)	0.016 (0.117)
Range	0.000 - 0.092	0.000 - 0.000	0.000 - 2.845	0.000 - 1.032	0.000 - 2.845
sIgE_f2						< 0.001
Mean (SD)	0.009 (0.066)	0.000 (0.000)	0.186 (0.829)	0.042 (0.148)	0.030 (0.277)
Range	0.000 - 0.640	0.000 - 0.000	0.000 - 6.849	0.000 - 1.091	0.000 - 6.849
sIgE_f3						< 0.001
Mean (SD)	0.001 (0.006)	0.000 (0.000)	0.016 (0.042)	0.006 (0.017)	0.003 (0.016)
Range	0.000 - 0.045	0.000 - 0.000	0.000 - 0.213	0.000 - 0.080	0.000 - 0.213
sIgE_f4						< 0.001
Mean (SD)	0.005 (0.076)	0.000 (0.000)	0.238 (0.945)	0.048 (0.222)	0.035 (0.324)
Range	0.000 - 1.280	0.000 - 0.000	0.000 - 8.417	0.000 - 2.543	0.000 - 8.417
sIgE_f13						0.002
Mean (SD)	0.017 (0.077)	0.002 (0.030)	1.591 (12.755)	0.107 (0.312)	0.187 (4.042)
Range	0.000 - 0.800	0.000 - 0.642	0.000 - 133.659	0.000 - 1.957	0.000 - 133.659
sIgE_f14						< 0.001
Mean (SD)	0.000 (0.003)	0.000 (0.000)	0.203 (0.946)	0.020 (0.092)	0.025 (0.306)
Range	0.000 - 0.040	0.000 - 0.000	0.000 - 8.505	0.000 - 0.833	0.000 - 8.505
sIgE_f17						< 0.001
Mean (SD)	0.267 (0.781)	0.007 (0.066)	9.076 (15.899)	0.921 (2.133)	1.174 (5.760)
Range	0.000 - 4.561	0.000 - 1.014	0.000 - 76.467	0.000 - 11.197	0.000 - 76.467
sIgE_f18						0.012
Mean (SD)	0.005 (0.042)	0.001 (0.015)	0.477 (4.469)	0.026 (0.219)	0.055 (1.416)
Range	0.000 - 0.686	0.000 - 0.322	0.000 - 46.879	0.000 - 2.901	0.000 - 46.879
sIgE_f20						< 0.001
Mean (SD)	0.010 (0.034)	0.000 (0.004)	0.399 (1.057)	0.043 (0.117)	0.052 (0.357)
Range	0.000 - 0.272	0.000 - 0.057	0.000 - 9.959	0.000 - 0.915	0.000 - 9.959
sIgE_f36						< 0.001
Mean (SD)	0.009 (0.026)	0.001 (0.005)	0.163 (0.763)	0.036 (0.112)	0.027 (0.250)
Range	0.000 - 0.235	0.000 - 0.067	0.000 - 7.754	0.000 - 1.051	0.000 - 7.754
gender2						0.229
females	150 (52.6%)	263 (55.7%)	58 (52.7%)	112 (47.5%)	583 (52.9%)
males	135 (47.4%)	209 (44.3%)	52 (47.3%)	124 (52.5%)	520 (47.1%)
age						0.001
Mean (SD)	49.396 (14.935)	51.639 (15.931)	45.611 (14.833)	51.412 (14.636)	50.410 (15.390)
Range	18.146 - 77.746	18.875 - 77.259	20.867 - 77.130	18.379 - 76.628	18.146 - 77.746
bmi2						< 0.001
Mean (SD)	26.126 (2.338)	23.553 (2.460)	31.668 (6.216)	28.599 (3.067)	26.107 (4.127)
Range	19.223 - 31.644	16.975 - 29.835	18.939 - 44.816	19.818 - 36.523	16.975 - 44.816
farmlive
	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
no	252 (88.4%)	382 (80.9%)	107 (97.3%)	219 (92.8%)	960 (87.0%)
yes	33 (11.6%)	90 (19.1%)	3 (2.7%)	17 (7.2%)	143 (13.0%)
family_allergy_hist						< 0.001
no	164 (57.5%)	361 (76.5%)	38 (34.5%)	132 (55.9%)	695 (63.0%)
yes	121 (42.5%)	111 (23.5%)	72 (65.5%)	104 (44.1%)	408 (37.0%)

# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)

With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <-  result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters

	0 (N=708)	1 (N=31)	2 (N=269)	3 (N=70)	Total (N=1078)	p value
sIgE_f1						< 0.001
Mean (SD)	0.008 (0.070)	0.527 (1.012)	0.088 (0.546)	1.446 (8.885)	0.136 (2.300)
Range	0.000 - 1.060	0.010 - 4.695	0.000 - 8.179	0.000 - 73.692	0.000 - 73.692
sIgE_f2						< 0.001
Mean (SD)	0.004 (0.035)	2.043 (2.909)	0.056 (0.149)	0.293 (1.632)	0.094 (0.728)
Range	0.000 - 0.551	0.034 - 13.006	0.000 - 0.850	0.000 - 13.623	0.000 - 13.623
sIgE_f3						< 0.001
Mean (SD)	0.001 (0.006)	0.243 (0.361)	0.011 (0.024)	0.067 (0.194)	0.015 (0.090)
Range	0.000 - 0.080	0.015 - 1.244	0.000 - 0.137	0.000 - 1.332	0.000 - 1.332
sIgE_f4						< 0.001
Mean (SD)	0.002 (0.014)	0.538 (1.328)	0.107 (0.316)	1.063 (2.256)	0.112 (0.687)
Range	0.000 - 0.224	0.030 - 7.445	0.000 - 2.275	0.000 - 12.512	0.000 - 12.512
sIgE_f13						< 0.001
Mean (SD)	0.022 (0.094)	6.986 (25.028)	0.287 (0.859)	1.913 (4.129)	0.411 (4.497)
Range	0.000 - 0.927	0.017 - 133.659	0.000 - 11.866	0.000 - 23.815	0.000 - 133.659
sIgE_f14						< 0.001
Mean (SD)	0.001 (0.007)	0.430 (1.026)	0.052 (0.182)	0.704 (2.127)	0.071 (0.601)
Range	0.000 - 0.160	0.000 - 4.468	0.000 - 1.475	0.000 - 12.386	0.000 - 12.386
sIgE_f17						< 0.001
Mean (SD)	0.150 (0.435)	1.714 (3.833)	4.567 (5.110)	25.396 (26.534)	2.937 (9.526)
Range	0.000 - 2.715	0.000 - 17.146	0.000 - 23.778	0.000 - 111.259	0.000 - 111.259
sIgE_f18						< 0.001
Mean (SD)	0.006 (0.069)	0.068 (0.137)	0.042 (0.217)	0.818 (5.599)	0.069 (1.436)
Range	0.000 - 1.678	0.000 - 0.585	0.000 - 2.901	0.000 - 46.879	0.000 - 46.879
sIgE_f20						< 0.001
Mean (SD)	0.008 (0.029)	0.392 (0.776)	0.161 (0.277)	1.179 (2.058)	0.133 (0.625)
Range	0.000 - 0.343	0.000 - 3.849	0.000 - 1.920	0.000 - 9.959	0.000 - 9.959
sIgE_f36						< 0.001
Mean (SD)	0.009 (0.028)	0.100 (0.180)	0.082 (0.157)	0.487 (1.335)	0.061 (0.368)
Range	0.000 - 0.277	0.000 - 0.845	0.000 - 1.051	0.000 - 7.754	0.000 - 7.754
gender2						0.354
females	408 (57.6%)	16 (51.6%)	149 (55.4%)	33 (47.1%)	606 (56.2%)
males	300 (42.4%)	15 (48.4%)	120 (44.6%)	37 (52.9%)	472 (43.8%)
age						< 0.001
Mean (SD)	49.324 (15.421)	48.398 (19.396)	44.784 (14.798)	40.863 (12.795)	47.615 (15.443)
Range	19.266 - 76.656	19.415 - 77.130	19.058 - 76.190	20.741 - 78.075	19.058 - 78.075
bmi2						< 0.001
Mean (SD)	25.960 (3.499)	28.986 (5.065)	27.843 (5.614)	30.970 (8.239)	26.842 (4.790)
Range	17.404 - 34.484	19.044 - 38.514	17.915 - 40.083	20.381 - 50.058	17.404 - 50.058
farmlive
	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
no	637 (90.0%)	29 (93.5%)	238 (88.5%)	64 (91.4%)	968 (89.8%)
yes	71 (10.0%)	2 (6.5%)	31 (11.5%)	6 (8.6%)	110 (10.2%)
family_allergy_hist						< 0.001
no	347 (49.0%)	8 (25.8%)	98 (36.4%)	22 (31.4%)	475 (44.1%)
yes	361 (51.0%)	23 (74.2%)	171 (63.6%)	48 (68.6%)	603 (55.9%)

# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)

Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <-  result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")

Table: Charachtaristcs of Clusters

	0 (N=134)	1 (N=14)	2 (N=55)	3 (N=591)	Total (N=794)	p value
sIgE_f1						< 0.001
Mean (SD)	0.053 (0.148)	0.017 (0.045)	0.005 (0.019)	0.000 (0.000)	0.010 (0.064)
Range	0.000 - 0.918	0.000 - 0.149	0.000 - 0.098	0.000 - 0.000	0.000 - 0.918
sIgE_f2						< 0.001
Mean (SD)	0.059 (0.166)	0.055 (0.156)	0.046 (0.290)	0.000 (0.000)	0.014 (0.106)
Range	0.000 - 1.091	0.000 - 0.559	0.000 - 2.143	0.000 - 0.000	0.000 - 2.143
sIgE_f3						< 0.001
Mean (SD)	0.008 (0.018)	0.017 (0.043)	0.004 (0.014)	0.000 (0.000)	0.002 (0.011)
Range	0.000 - 0.067	0.000 - 0.134	0.000 - 0.065	0.000 - 0.000	0.000 - 0.134
sIgE_f4						< 0.001
Mean (SD)	0.043 (0.148)	0.254 (0.653)	0.097 (0.462)	0.000 (0.000)	0.019 (0.164)
Range	0.000 - 1.129	0.000 - 2.031	0.000 - 2.543	0.000 - 0.000	0.000 - 2.543
sIgE_f13						< 0.001
Mean (SD)	0.046 (0.165)	10.907 (39.964)	0.099 (0.280)	0.000 (0.000)	0.207 (5.315)
Range	0.000 - 1.596	0.000 - 149.746	0.000 - 1.486	0.000 - 0.000	0.000 - 149.746
sIgE_f14						< 0.001
Mean (SD)	0.011 (0.047)	0.456 (1.336)	0.028 (0.122)	0.000 (0.000)	0.012 (0.185)
Range	0.000 - 0.366	0.000 - 4.886	0.000 - 0.728	0.000 - 0.000	0.000 - 4.886
sIgE_f17						< 0.001
Mean (SD)	0.408 (1.130)	4.284 (10.816)	1.412 (4.447)	0.000 (0.000)	0.242 (1.977)
Range	0.000 - 7.065	0.000 - 36.193	0.000 - 19.736	0.000 - 0.000	0.000 - 36.193
sIgE_f18						< 0.001
Mean (SD)	0.007 (0.032)	0.096 (0.248)	0.016 (0.050)	0.000 (0.000)	0.004 (0.039)
Range	0.000 - 0.322	0.000 - 0.868	0.000 - 0.261	0.000 - 0.000	0.000 - 0.868
sIgE_f20						< 0.001
Mean (SD)	0.025 (0.073)	0.366 (0.762)	0.067 (0.171)	0.000 (0.000)	0.015 (0.122)
Range	0.000 - 0.520	0.000 - 2.149	0.000 - 0.849	0.000 - 0.000	0.000 - 2.149
sIgE_f36						< 0.001
Mean (SD)	0.021 (0.060)	0.140 (0.315)	0.038 (0.121)	0.000 (0.000)	0.009 (0.061)
Range	0.000 - 0.366	0.000 - 1.081	0.000 - 0.734	0.000 - 0.000	0.000 - 1.081
gender2						0.039
females	57 (42.5%)	6 (42.9%)	26 (47.3%)	327 (55.3%)	416 (52.4%)
males	77 (57.5%)	8 (57.1%)	29 (52.7%)	264 (44.7%)	378 (47.6%)
age						0.472
Mean (SD)	50.340 (15.466)	50.315 (14.955)	47.934 (14.331)	51.261 (15.644)	50.858 (15.512)
Range	18.379 - 76.877	22.091 - 74.242	19.428 - 74.099	18.146 - 77.746	18.146 - 77.746
bmi2						< 0.001
Mean (SD)	28.253 (3.789)	37.507 (7.834)	32.104 (3.784)	24.719 (2.772)	26.053 (4.136)
Range	18.904 - 37.109	22.097 - 46.094	23.356 - 40.164	16.975 - 32.076	16.975 - 46.094
farmlive
	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
no	112 (83.6%)	13 (92.9%)	48 (87.3%)	507 (85.8%)	680 (85.6%)
yes	22 (16.4%)	1 (7.1%)	7 (12.7%)	84 (14.2%)	114 (14.4%)
family_allergy_hist						0.978
no	93 (69.4%)	10 (71.4%)	39 (70.9%)	405 (68.5%)	547 (68.9%)
yes	41 (30.6%)	4 (28.6%)	16 (29.1%)	186 (31.5%)	247 (31.1%)

# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")

Charachtiristic Analysis

result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)

adding the id varaible

resultft_DEL_all$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')